Automated Inspection and Verification of Electric Motor Weld Quality

2. The method according to claim 1, wherein the camera is a 2D area camera.

3. The method according to claim 1, wherein the camera is a 2D line scan camera.

4. The method according to claim 1 further comprising transporting the electric motor stator across a scanning area of the camera such that a 2D camera scans and acquires the images of the plurality of welds as the electric motor stator moves relative to the 2D camera.

5. The method according to claim 4, wherein the electric motor stator is transported across the scanning area of the 2D camera with a conveyor.

7. The method according to claim 6, wherein the plurality of welds for each of the plurality of electric motors is more than 100 welds.

8. The method according to claim 7, wherein each of the adjacent electrical wires for each of the plurality of electric motors is laser welded to form the plurality of welds.

9. The method according to claim 1, wherein the weld defects comprise at least one of voids, pits, cracks, weld size too small, weld size too large, and enamel contamination.

11. The method according to claim 1, wherein the adjacent electrical wires comprise adjacent hairpin segments.

12. The method according to claim 11, wherein the adjacent hairpin segments are polymer coated with de-coated ends.

13. The method according to claim 12, wherein the at least one neural network determines if any of the plurality of welds have a polymer contamination weld defect.

14. The method according to claim 1 further comprising generating an alert and an identification when a weld defect is determined for at least one of the plurality of welds, wherein the identification comprises a position of the weld defect on the stator winding.

16. The method according to claim 15, wherein the weld defects are selected from the group consisting of voids, pits, cracks, weld size too small, weld size too large, and enamel contamination.

17. The method according to claim 15, wherein the alert comprises an identification of a weld defect type and a position of the weld defect on the stator winding.

19. The method according to claim 18 further comprising training the at least one neural network with an identified weld defect.